Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons

Ju Tian, Ryan Huang, Jeremiah Y. Cohen, Fumitaka Osakada, Dmitry Kobak, Christian K. Machens, Edward M. Callaway, Naoshige Uchida, Mitsuko Watabe-Uchida

Research output: Contribution to journalArticlepeer-review

Abstract

Dopamine neurons encode the difference between actual and predicted reward, or reward prediction error (RPE). Although many models have been proposed to account for this computation, it has been difficult to test these models experimentally. Here we established an awake electrophysiological recording system, combined with rabies virus and optogenetic cell-type identification, to characterize the firing patterns of monosynaptic inputs to dopamine neurons while mice performed classical conditioning tasks. We found that each variable required to compute RPE, including actual and predicted reward, was distributed in input neurons in multiple brain areas. Further, many input neurons across brain areas signaled combinations of these variables. These results demonstrate that even simple arithmetic computations such as RPE are not localized in specific brain areas but, rather, distributed across multiple nodes in a brain-wide network. Our systematic method to examine both activity and connectivity revealed unexpected redundancy for a simple computation in the brain.

Original languageEnglish (US)
Pages (from-to)1374-1389
Number of pages16
JournalNeuron
Volume91
Issue number6
DOIs
StatePublished - Sep 21 2016

ASJC Scopus subject areas

  • Neuroscience(all)

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